"This notebook covers how to use few shot examples in chat models.\n",
"This notebook covers how to use few shot examples in chat models. There does not appear to be solid consensus on how best to do few shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the [FewShotChatMessagePromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.few_shot.FewShotChatMessagePromptTemplate.html) as a flexible starting point, and you can modify or replace them as you see fit.\n",
"\n",
"There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abstractions around this yet but rather using existing abstractions."
"The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.\n",
"\n",
"\n",
"**Note:** The following code examples are for chat models. For similar few-shot prompt examples for completion models (LLMs), see the [few-shot prompt templates](few_shot_examples) guide."
]
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"## Alternating Human/AI messages\n",
"The first way of doing few shot prompting relies on using alternating human/ai messages. See an example of this below."
"### Fixed Examples\n",
"\n",
"The most basic (and common) few-shot prompting technique is to use a fixed prompt example. This way you can select a chain, evaluate it, and avoid worrying about additional moving parts in production.\n",
"\n",
"The basic components of the template are:\n",
"- `examples`: A list of dictionary examples to include in the final prompt.\n",
"- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
"\n",
"Below is a simple demonstration. First, import the modules for this example:"
"AIMessage(content=' Triangles do not have a \"square\". A square refers to a shape with 4 equal sides and 4 right angles. Triangles have 3 sides and 3 angles.\\n\\nThe area of a triangle can be calculated using the formula:\\n\\nA = 1/2 * b * h\\n\\nWhere:\\n\\nA is the area \\nb is the base (the length of one of the sides)\\nh is the height (the length from the base to the opposite vertex)\\n\\nSo the area depends on the specific dimensions of the triangle. There is no single \"square of a triangle\". The area can vary greatly between different triangles.', additional_kwargs={}, example=False)"
"chain.invoke({\"input\": \"What's the square of a triangle?\"})"
]
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"## Dynamic Few-shot Prompting\n",
"\n",
"Sometimes you may want to condition which examples are shown based on the input. For this, you can replace the `examples` with an `example_selector`. The other components remain the same as above! To review, the dynamic few-shot prompt template would look like:\n",
"\n",
"- `example_selector`: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement the [BaseExampleSelector](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.example_selector.base.BaseExampleSelector.html#langchain.prompts.example_selector.base.BaseExampleSelector) interface. A common example is the vectorstore-backed [SemanticSimilarityExampleSelector](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector.html#langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector)\n",
"- `example_prompt`: convert each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
"\n",
"These once again can be composed with other messages and chat templates to assemble your final prompt."
]
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"template = \"You are a helpful assistant that translates english to pirate.\"\n",
"OpenAI provides an optional `name` parameter that they also recommend using in conjunction with system messages to do few shot prompting. Here is an example of how to do that below."
"Assemble the prompt template, using the `example_selector` created above."
William FH
11 months ago
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